COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level
COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompani...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3658 |
| _version_ | 1827666700916490240 |
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| author | Ioannis Kavouras Maria Kaselimi Eftychios Protopapadakis Nikolaos Bakalos Nikolaos Doulamis Anastasios Doulamis |
| author_facet | Ioannis Kavouras Maria Kaselimi Eftychios Protopapadakis Nikolaos Bakalos Nikolaos Doulamis Anastasios Doulamis |
| author_sort | Ioannis Kavouras |
| collection | DOAJ |
| description | COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models. |
| first_indexed | 2024-03-10T01:55:24Z |
| format | Article |
| id | doaj.art-d8a36ba4b6574a67988eed071d0f1800 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:55:24Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d8a36ba4b6574a67988eed071d0f18002023-11-23T12:58:58ZengMDPI AGSensors1424-82202022-05-012210365810.3390/s22103658COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European LevelIoannis Kavouras0Maria Kaselimi1Eftychios Protopapadakis2Nikolaos Bakalos3Nikolaos Doulamis4Anastasios Doulamis5School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceSchool of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceSchool of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceSchool of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceSchool of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceSchool of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceCOVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models.https://www.mdpi.com/1424-8220/22/10/3658COVID-19 policiesdeep learningtime-series predictionCOVID-19 reported casesdata-driven pandemic interventions |
| spellingShingle | Ioannis Kavouras Maria Kaselimi Eftychios Protopapadakis Nikolaos Bakalos Nikolaos Doulamis Anastasios Doulamis COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level Sensors COVID-19 policies deep learning time-series prediction COVID-19 reported cases data-driven pandemic interventions |
| title | COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level |
| title_full | COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level |
| title_fullStr | COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level |
| title_full_unstemmed | COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level |
| title_short | COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level |
| title_sort | covid 19 spatio temporal evolution using deep learning at a european level |
| topic | COVID-19 policies deep learning time-series prediction COVID-19 reported cases data-driven pandemic interventions |
| url | https://www.mdpi.com/1424-8220/22/10/3658 |
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